predict price of car from vehicles dataset

HIGH PERFORMANCE ANALYTICS USING

SAP HANAVehicle (dataset)

Project Team (Group 1):

- Sumit Kumar Saini

- Mohamed Salihdeen- Subhor Verma- Yogesh Dangi

DATA SET• There are two tables in our dataset• Table – 1 Vehicle Master

• It is our original vehicle dataset with all basic details about vehicle such as VIN, make, model, mileage, price, production date, no of services till date and age.

• Table – 2 Vehicle transactional data• It contains combined columns of other datasets to better visualize and

analyze the features and prediction. It contains primary column from master table i.e VIN and price. Other features are Insured amount, premium, service type, Dealer No, job class and max education.

HYPOTHESES• Hypothesis 1 – Does the age of the car and the mileage is making an

impact on the insurance amount of the car.

• Hypothesis 2 – Does production year or age of the car is impacting the premium for the car.

• Hypothesis 3- Does the model of the car affecting the premium amount.

• Hypothesis 4- Does the job class has any affect on the price of the car they purchase.

THEOREM 1

THEOREM 1 (CONTD.)

THEOREM 2

THEOREM 3

THEOREM 4

THEOREMS• Theorem I - We can see that as the age of the car is increasing, the

insurance amount of the car is decreasing.

• Theorem II - We can see that as the age of the car is increasing the premium for the car is decreasing.

• Theorem III - We can see that the expensive cars have higher premium. Like Audi has higher premium amount than Chevy cars

• Theorem IV – We can see that doctors or lawyers are purchasing more expensive cars than blue-collard people.

TOOLS USED

• SAP HANA - This is used for data extraction and building views ie:- attribute view and analytical views.

• SAP LUMIRA- This is used for building Visualizations.

• SAP Predictive Analytical tools – This is used for predictive analysis based on the input features.

SAP PREDICTIVE ANALYTICAL TOOL - MODELER

• We have regression analysis and decision tree to build the model and predict the values. Our target value is price.


• Predictive power KI is 91.55% i.e model predict the price of car based on input features 91.55% accurately.

• Prediction confidence KR is 98.04% i.e model can be reliably used to continue on the data set.


• Regression Model


• Decision tree model


• Statistical Report


• Influence on target of input feature


• Maximum Variable contribution


• Model predicted values

SAP PREDICTIVE ANALYTICAL TOOL – EXPERT ANALYTICS

• We have performed auto regression analysis in expert analytics keeping price as target variable.


• Feature selection


• Variable contribution


• Model Graph

predict price of car from vehicles dataset

Documents